Persistent memory + rollback + audit trail for LlamaIndex agents using Novyx Core
Project description
novyx-llamaindex
Persistent memory + rollback + audit trail for LlamaIndex agents, powered by Novyx Core.
Install
pip install novyx-llamaindex
Quick Start
ChatStore (drop-in for SimpleChatStore)
from novyx_llamaindex import NovyxChatStore
from llama_index.core.memory import ChatMemoryBuffer
chat_store = NovyxChatStore(api_key="nram_your_key")
memory = ChatMemoryBuffer.from_defaults(
chat_store=chat_store,
chat_store_key="user-123",
token_limit=3000,
)
BaseMemory (semantic recall)
from novyx_llamaindex import NovyxMemory
memory = NovyxMemory.from_defaults(
api_key="nram_your_key",
session_id="user-123",
top_k=5,
)
# Use with any LlamaIndex chat engine
engine = SimpleChatEngine.from_defaults(memory=memory)
response = engine.chat("Hello!")
Retriever (RAG pipeline)
from novyx_llamaindex import NovyxRetriever
retriever = NovyxRetriever(api_key="nram_your_key", top_k=5)
nodes = retriever.retrieve("What are the user's preferences?")
Features
- Persistent: Memories survive across sessions, restarts, and deployments
- Semantic:
recall()finds relevant memories by meaning, not just keywords - Rollback: Undo memory mistakes with
memory.rollback("2 hours ago") - Audit Trail: Every operation is SHA-256 hashed and logged
- Cross-session: Agent A learns it, Agent B knows it (via Context Spaces)
API Reference
| Class | Base Class | Description |
|---|---|---|
NovyxChatStore |
BaseChatStore |
Drop-in persistent chat store |
NovyxMemory |
BaseMemory |
Semantic memory with recall on get() |
NovyxRetriever |
BaseRetriever |
RAG retriever over Novyx memories |
NovyxMemoryClient |
— | Low-level client for direct Novyx API access |
License
MIT
Project details
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